India’s next agricultural technology opportunity may lie beyond the farm. Agritech companies have developed tools for crop monitoring, pest detection, and yield prediction, but a bigger challenge remains: connecting farm-level intelligence to the supply chain that determines where produce goes, how it is priced, and who captures its value.
Produce moves through farmers, aggregators, warehouses, traders, processors and retailers, with information often recorded separately at each stage. AI could help businesses forecast availability, plan procurement and assess demand. But these capabilities depend on access to reliable data across a chain where records, systems and commercial interests remain fragmented.
Post-harvest losses highlight the extent of agricultural inefficiencies in India. A 2022 NABARD Consultancy Services (NABCONS) study estimated losses of 6–15% for fruits and 5–12% for vegetables between 2020 and 2022.
The study covered 54 agricultural commodities across 292 districts and assessed losses at multiple stages, including harvesting, transportation and storage. These figures don’t show how much AI could prevent, but they highlight the operational challenges facing India’s agricultural value chain.
“The problem is less a complete absence of data than its fragmentation. Farmers, FPOs, mandis, aggregators, logistics firms, processors and retailers use different recordkeeping methods, languages, applications and enterprise systems. Requiring every stakeholder to adopt one application is unlikely to succeed,” says Krishna Kumar, founder and CEO of Cropin.
Fragmentation also raises questions about data ownership. “The real damage happening for the entire ecosystem is not just because of fragmented operations, but because data sits in private books of these fragmented players,” says Rama Kancharapu, founder, CEO and CPO of agricultural market digitisation platform ONO.
Connecting these records is therefore as much a question of access and incentives as it is of technology.
From Farm Intelligence to Supply Chain Decisions
Farm-level intelligence becomes more commercially useful when it informs decisions beyond cultivation. Knowing how a crop is growing helps assess its likely performance, but processors and buyers also need to know how much produce will be available, where it will come from, and when they can procure it.
“The emerging phase is about connecting that intelligence directly to business decisions across the supply chain,” Kumar says.
Applications are already extending into market forecasting and operations. “Immediate impact one can already witness: price predictions, supply and demand predictions, yield forecasting, quality assessment, fraud prevention and logistics optimisation,” Kancharapu says.
ONO has captured more than three seasons of market data through its software-as-a-service platform, according to Kancharapu. He claims the platform enables price, arrival, supply and demand predictions with more than 90% accuracy. This remains a company-reported figure. The provided information does not establish the methodology or evaluation criteria.
The government is also building digital infrastructure for agriculture. The Ministry of Agriculture and Farmers Welfare reported that it had created more than 10.31 crore Farmer IDs under the Digital Agriculture Mission as of August 3, 2026. These records can help structure farmer and agricultural information, but they do not automatically capture every transaction, quality assessment or movement of produce between farms, markets, warehouses and processors.
That distinction matters. A forecast is useful only if businesses can act on it, whether by adjusting procurement, planning storage or coordinating transport. Better predictions alone cannot guarantee that the right produce reaches the right buyer at the right time.
Why Traceability Remains Difficult
Traceability presents a related challenge. Produce may be pooled from multiple farms, graded, repacked or processed before reaching buyers. If these changes aren’t recorded consistently, the link between a finished product and its source can become hard to establish.
India already has digital infrastructure for specific export supply chains. APEDA’s HortiNet system supports certification and traceability for selected horticultural exports, including grapes, pomegranates and mangoes. It stores forward- and backwards-traceability and quality-assurance information entered by stakeholders. APEDA also disclaims responsibility for errors arising from incorrect information supplied by users.
The system illustrates both the potential and the limitations of digital traceability. Records can be stored and linked, but their reliability depends on the accuracy of the information entered.
“AI and satellite intelligence can validate some claims about cultivation, crop progression, geography and harvest readiness. They cannot, on their own, reconstruct an undocumented physical journey or prove every custody transfer,” Kumar cautions.
Satellite imagery can help assess cultivation conditions and flag inconsistencies in production claims. AI can analyse records and identify discrepancies. Neither, however, can independently establish what happened to a consignment if its movement was never documented. End-to-end traceability requires reliable records at each handover, supported by verification and auditable systems.
The Economics of Sharing Data
Even where better data could improve operations, stakeholders need a reason to share it. Traders and aggregators may be reluctant to disclose commercially sensitive information about procurement volumes, supplier relationships and prices, particularly if sharing it offers little immediate benefit.
For ONO, adoption depends on creating value across the chain rather than asking participants to contribute information without a clear return.
“An integrated solution that creates value at every handshake and captures data is what makes it naturally adopt and drive long-term value,” Kancharapu says.
The economics of agricultural data businesses presents another obstacle. “This challenge has been that, you know, data has not been a very economically viable model, because you have no access to it,” says Shubham Jhuria of Aeravti Ventures, highlighting the difficulty of building a viable business around information that remains inaccessible.
A farmer may record cultivation details, an aggregator may maintain procurement records and a warehouse may track inventory. Yet the greatest commercial benefit from connecting these datasets could accrue to processors, exporters or retailers that use the information to make better decisions. If data generators do not see a meaningful benefit, participation may remain uneven.
Common data standards, shared identifiers and connections between existing platforms could help information move across the supply chain without requiring every participant to adopt the same application. But the cost of collecting and maintaining records, data-access terms, and how commercial benefits are distributed will shape whether these systems gain traction.
Who Captures the Value?
Better visibility into crop availability, quality and demand could help businesses plan procurement, coordinate logistics and reduce avoidable inefficiencies. Kancharapu expects further applications in price prediction, supply-demand intelligence and fraud prevention over the next three to five years.
The larger question is whether those improvements translate into benefits for farmers. Better market information could help producers make decisions, but outcomes also depend on realised prices, input costs, rejection rates and the terms on which they sell their produce. If connected data mainly helps larger businesses reduce costs, the benefits may not reach the people who grow the crops.
India’s next agricultural AI opportunity lies in connecting information across the journey from cultivation to sale. Government registries and export traceability systems provide parts of the digital foundation, while agritech companies are exploring how market data can inform commercial decisions. The challenge is to make those systems work across fragmented networks of participants and create incentives for them to share accurate information.
AI may help businesses see more of the supply chain. Whether that visibility translates into better outcomes depends on who can access the data, who acts on it, and who ultimately captures the value.